Outcomes of Management of Proximal Humeral Fractures with Patient-Specific, Evidence-Based Treatment Algorithms

Background: Previous studies have identified risk factors for different types of treatment of proximal humeral fracture (PHF) and allowed the development of a patient-specific, evidence-based treatment algorithm with the potential of improving overall outcomes and reducing complications. The purpose of this study was to evaluate the results and complications of treating PHF using this algorithmic approach. Methods: All patients with isolated PHF between 2014 and 2017 were included and prospectively followed. The initial treatment algorithm (Version 1 [V1]) based on patients' functional needs, bone quality, and type of fracture was refined after 2 years (Version 2 [V2]). Adherence to protocol, clinical outcomes, and complications were analyzed at a 1-year follow-up. Results: The study included 334 patients (mean age, 66 years; 68% female): 226 were treated nonoperatively; 65, with open reduction and internal fixation (ORIF); 39, with reverse total shoulder arthroplasty (RTSA); and 4, with hemiarthroplasty. At 1 year, the preinjury EuroQol 5-Dimension (EQ-5D) values were regained (0.88 and 0.89, respectively) and the mean relative Constant Score (CS) and Subjective Shoulder Value (SSV) (and standard deviation [SD]) were 96% +/- 21% and 85% +/- 16%. Overall complications and revision rates were 19% and 13%. Treatment conforming to the algorithm outperformed non-conforming treatment with respect to relative CS (97% versus 88%, p = 0.016), complication rates (16.3% versus 30.8%, p = 0.014), and revision rates (10.6% versus 26.9%, p < 0.001). Conclusions: Treating PHF using a patient-specific, evidence-based algorithm restored preinjury quality of life as measured with the EQ-5D and approximately 90% normal shoulders as measured with the relative CS and the SSV. Adherence to the treatment algorithm was associated with significantly better clinical outcomes and substantially reduced complication and revision rates.